Independent Load Forecast Workshop July 25, 2016

Stakeholder Comments

Stakeholder Comments Documents regarding multicollinearity, heteroscedasticity, first difference models, and comparison of 2015 and 2016 models are provided separately Inclusion of 2015 data on the load forecast comparison results and results of Louisiana sales+CHP modeling are included in this presentation

Stakeholder Comments We were asked a question regarding incorporating a significant change in load if LSEs provide feedback explaining the change We can make a load adjustment to the forecast based on such changes. This should be done with care to avoid double counting (if the macroeconomic forecast already reflects it) and to ensure the allocation factors assign the load change to the proper LRZ in the state

Forecast Comparison 2016-2025 2015 ILF vs. Module E vs. 2015 Actual

Loads Comparison Comparison charts have been made to show 2015 actual zonal peak/annual loads and projections from ILF The historical data source (data 1) we used for the 2014 and 2015 ILF studies were discontinued We have been provided with historical data from a different source (data 4) Discrepancies between data 1 and 4 exist and may or may not be significant.

Historical Hourly Loads Data 1 Data 4 Metered load with EE/DR/DG adjustment; Does not include voluntary LMR data. Used for 2014 and 2015 ILF peak analysis; Historical hourly loads for LRZ 8,9 and 10 (prior to LRZ formation) were derived; Contains 2010 to 2014 historical hourly data. SERVM Historical Hourly Load; Contains voluntary LMR data starting May 2014; Contains 2010 to 2015 historical hourly load data; The recorded hourly and annual loads do not exact match data 1’s records; Peak demand may not occur at the same time as in data 1.

Summer Peak Discrepancies Between Data Sources (2014) LRZ Peak Demand (1) Peak Demand (4) Difference Load Factor (1) Load Factor (4) 2014(1) PT 2014(4) PT 1 17,018 16,937 -0.5% 67% 66% 7/21/2014 15:00 7/21/2014 14:00 2 11,730 11,720 -0.1% 63% 7/22/2014 16:00 3 8,283 8,244 65% 9/4/2014 16:00 4 9,563 9,588 0.3% 60% 8/25/2014 15:00 5 8,487 8,507 0.2% 58% 8/25/2014 16:00 6 17,170 17,204 68% 9/5/2014 15:00 9/5/2014 14:00 7 19,293 19,246 -0.2% 59% 8 7,058 7,172 1.6% 8/25/2014 13:00 8/26/2014 15:00 9 19,173 19,220 8/22/2014 16:00 10 4,297 4,277 57% 8/6/2014 16:00 8/20/2014 16:00 MISO 114,709 114,285 -0.4% Red indicates shift in time of peak demand

Forecast Comparisons ILF Peak MISO Module E Peak Allocates state-level forecasts to LRZ levels; Uses zonal non-coincident factors and coincidence factors to estimate zonal peak loads and system-wide peak load; Net load: with EE/DR/DG adjustment; Gross load: without EE/DR/DG adjustment. Aggregates asset owner level loads to LRZ levels; Applies intra-zonal/MISO-wide coincidence factors to convert asset owner level peak loads to zonal peaks and system-wide peak load; Does not include DR/DG adjustment, may include EE adjustment.

Comparison Charts Module E – solid black line ILF Gross – dashed black line ILF Gross high/low bands – hashed area ILF Net – dashed red line ILF Net high/low bands – solid area 2014 actual value from data 1 – blue diamond; 2014 and 2015 actual loads from data 4 – red square; Metered loads, transmission losses excluded Historical data based on actual weather conditions; forecasts assume normal weather

SUFG Peak Gross & Net Forecast vs. Module E vs. 2015 Actual—LRZ 1

SUFG Peak Gross & Net Forecast vs. Module E vs. 2015 Actual—LRZ 2

SUFG Peak Gross & Net Forecast vs. Module E vs. 2015 Actual—LRZ 3

SUFG Peak Gross & Net Forecast vs. Module E vs. 2015 Actual—LRZ 4

SUFG Peak Gross & Net Forecast vs. Module E vs. 2015 Actual—LRZ 5

SUFG Peak Gross & Net Forecast vs. Module E vs. 2015 Actual—LRZ 6

SUFG Peak Gross & Net Forecast vs. Module E vs. 2015 Actual—LRZ 7

SUFG Peak Gross & Net Forecast vs. Module E vs. 2015 Actual—LRZ 8

SUFG Peak Gross & Net Forecast vs. Module E vs. 2015 Actual—LRZ 9

SUFG Peak Gross & Net Forecast vs. Module E vs. 2015 Actual—LRZ 10

SUFG Peak Gross & Net Forecast vs. Module E vs. 2015 Actual—MISO

Module E Data PY2016-2017 Module E forecasts provide a series of projections of seasonal loads at the asset owner level (24 months followed by 8 years of summer and winter loads); Winter loads covers December, January, February, March, April and May; Summer loads cover June, July, August, September, October and November; Transmission loss is included; January to May 2016 monthly loads were from PY2015-2016.

Forecast Comparisons ILF MISO Module E Allocates state-level retail sales forecasts to LRZ levels Adjust zonal retail sales to metered level sales by adding zonal distribution loss Net load: with EE/DR/DG adjustment Gross load: without EE/DR/DG adjustment Forecast loads by calendar year Aggregates asset owner level loads to LRZ levels Are on a planning year basis that does not correspond to calendar year Are at the asset level (includes transmission losses) Does not include DR/DG adjustment, may include EE adjustment

Module E Data Adjustments Winter forecasts cover two calendar years (December-May) We assumed 5/6 of the winter load was in the 1st calendar year and 1/6 was in the 2nd Forecast was reduced by the transmission loss percentage to represent metered load level (as in ILF) Loss percentage is for peak demand, not annual energy This likely overstates losses, resulting in too large of a reduction

Annual Load Discrepancies Between Data 4 and Data 1 2010 2011 2012 2013 2014 1 4.88% -1.02% -0.90% -0.89% -1.01% 2 -0.40% -0.42% -0.91% -0.73% 3 -0.63% -0.81% -0.88% -0.65% -0.66% 4 -0.32% -0.18% -0.45% -0.43% -0.34% 5 -0.21% -0.11% 0.05% 0.17% 6 3.76% -6.14% -3.15% -3.12% -2.37% 7 -1.92% -1.06% -1.71% -1.82% -1.10% 8 -6.73% -6.26% -6.51% -9.68% -1.04% 9 -1.42% -1.08% -2.34% -2.86% -0.44% 10 -1.30% -1.78% -1.73% -18.12% -2.25% MISO 0.13% -1.93% -1.79% -2.62% -1.00% Negative values indicate that Data 4 are smaller than Data 1

Notes on Data Differences We have not had an opportunity to determine source of the discrepancies The 2014 and 2015 ILFs were calibrated to Data 1; calibration to Data 4 would lead to generally lower projections with similar trajectories

Comparison Charts Module E – solid black line ILF Gross – dashed black line ILF Gross high/low bands – hashed area ILF Net – dashed red line ILF Net high/low bands – solid area 2014 actual value from data 1 – blue diamond; 2014 and 2015 actual loads from data 4 – red square; Metered loads, transmission losses excluded Historical data based on actual weather conditions; forecasts assume normal weather

SUFG Annual Gross & Net Forecast vs. Module E vs. 2015 Actual—LRZ 1

SUFG Annual Gross & Net Forecast vs. Module E vs. 2015 Actual—LRZ 2

SUFG Annual Gross & Net Forecast vs. Module E vs. 2015 Actual—LRZ 3

SUFG Annual Gross & Net Forecast vs. Module E vs. 2015 Actual—LRZ 4

SUFG Annual Gross & Net Forecast vs. Module E vs. 2015 Actual—LRZ 5

SUFG Annual Gross & Net Forecast vs. Module E vs. 2015 Actual—LRZ 6

SUFG Annual Gross & Net Forecast vs. Module E vs. 2015 Actual—LRZ 7

SUFG Annual Gross & Net Forecast vs. Module E vs. 2015 Actual—LRZ 8

SUFG Annual Gross & Net Forecast vs. Module E vs. 2015 Actual—LRZ 9

SUFG Annual Gross & Net Forecast vs. Module E vs. 2015 Actual—LRZ 10

SUFG Annual Gross & Net Forecast vs. Module E vs. 2015 Actual—MISO

Draft State-level Forecasts

State Forecasts The state forecasts shown here are gross forecasts in that they do not include an adjustment for EE/DR/DG 2015 and 2016 forecasts are provided to show how different/similar they are

Arkansas Retail Sales (GWh) 2015 Forecast 2016-2025 projected CAGR - 1.00% 2016 Forecast 2017-2026 projected CAGR - 1.07% 1990-2014 actual CAGR -2.29% CAGR – Compound Annual Growth Rate

Illinois Retail Sales (GWh) 2015 Forecast 2016-2025 projected CAGR - 0.63% 2016 Forecast 2017-2026 projected CAGR - 0.64% 1990-2014 actual CAGR - 1.00% CAGR – Compound Annual Growth Rate

Indiana Retail Sales (GWh) 2015 Forecast 2016-2025 projected CAGR – 1.29% 2016 Forecast 2017-2026 projected CAGR – 1.41% 1990-2014 actual CAGR - 1.55% CAGR – Compound Annual Growth Rate

Iowa Retail Sales (GWh) 2015 Forecast 2016-2025 projected CAGR – 1.60% 2016 Forecast 2017-2026 projected CAGR – 1.70% 1990-2014 actual CAGR - 1.99% CAGR – Compound Annual Growth Rate

Kentucky Retail Sales (GWh) 2015 Forecast 2016-2025 projected CAGR – 1.09% 2016 Forecast 2017-2026 projected CAGR – 1.20% 1990-2014 actual CAGR – 1.07% CAGR – Compound Annual Growth Rate

Louisiana Retail Sales (GWh) LA will be discussed later

Michigan Retail Sales (GWh) 2015 Forecast 2016-2025 projected CAGR – 0.88% 2016 Forecast 2017-2026 projected CAGR – 0.98% 1990-2014 actual CAGR – 0.95% CAGR – Compound Annual Growth Rate

Minnesota Retail Sales (GWh) 2015 Forecast 2016-2025 projected CAGR – 1.67% 2016 Forecast 2017-2026 projected CAGR – 1.68% 1990-2014 actual CAGR – 1.58% CAGR – Compound Annual Growth Rate

Mississippi Retail Sales (GWh) 2015 Forecast 2016-2025 projected CAGR – 1.76% 2016 Forecast 2017-2026 projected CAGR – 1.63% 1990-2014 actual CAGR – 1.81% CAGR – Compound Annual Growth Rate

Missouri Retail Sales (GWh) 2015 Forecast 2016-2025 projected CAGR – 1.18% 2016 Forecast 2017-2026 projected CAGR – 1.25% 1990-2014 actual CAGR – 1.86% CAGR – Compound Annual Growth Rate

Montana Retail Sales (GWh) 2015 Forecast 2016-2025 projected CAGR – 1.82% 2016 Forecast 2017-2026 projected CAGR – 1.90% 1990-2014 actual CAGR – 0.30% CAGR – Compound Annual Growth Rate

North Dakota Retail Sales (GWh) 2015 Forecast 2016-2025 projected CAGR – 1.08% 2016 Forecast 2017-2026 projected CAGR – 1.55% 1990-2014 actual CAGR – 4.06% CAGR – Compound Annual Growth Rate

South Dakota Retail Sales (GWh) 2015 Forecast 2016-2025 projected CAGR – 2.02% 2016 Forecast 2017-2026 projected CAGR – 2.17% 1990-2014 actual CAGR – 2.82% CAGR – Compound Annual Growth Rate

Texas Retail Sales (GWh) 2015 Forecast 2016-2025 projected CAGR – 1.91% 2016 Forecast 2017-2026 projected CAGR – 2.00% 1990-2014 actual CAGR – 2.09% CAGR – Compound Annual Growth Rate

Wisconsin Retail Sales (GWh) 2015 Forecast 2016-2025 projected CAGR – 1.49% 2016 Forecast 2017-2026 projected CAGR – 1.53% 1990-2014 actual CAGR – 1.45% CAGR – Compound Annual Growth Rate

Elasticity at 2014 (weather at means) Louisiana Model Model presented in the last workshop - Personal Income used as macro economic variable Dependent Variable: ELECTRICITY_SALES Method: Least Squares Sample: 1990 2014 Included observations: 25   Variable Coefficient Std. Error t-Statistic Prob. Elasticity at 2014 (weather at means) C 74640.57 7268.002 10.26975 0.0000 @MOVAV(REAL_ELECTRICITY_PRICE,3) -4215.296 434.2424 -9.707242 -0.3385 REAL_INCOME 0.000115 0.0000123 9.370875 0.2503 CDD 4.621522 1.465265 3.154052 0.0050 0.1747 HDD 4.264524 1.328142 3.210895 0.0044 0.0839 R-squared 0.971777 Mean dependent var 77122.65 Adjusted R-squared 0.966132 S.D. dependent var 6902.32 S.E. of regression 1270.255 Durbin-Watson stat 1.802795 F-statistic 172.1575 Prob (F-statistic) 0.000000

Louisiana Model Problems with Previous Model Personal Income has a relatively low elasticity (0.25) and a low growth rate (2.05% for the period of 2017-2026). The model produces an unreasonably low electricity sales forecast of -0.11% over the period of 2017-2026. Alternative approaches were tested and will be presented.

Elasticity at 2014 (weather at means) Louisiana Model Option 1 Electricity sales as dependent variable GSP as macro-economic variable 2005 data dropped as an outliner (Hurricanes Katrina and Rita) Dependent Variable: ELECTRICITY_SALES Method: Least Squares Sample: 1990 2004 2006 2014 Included observations: 24   Variable Coefficient Std. Error t-Statistic Prob. Elasticity at 2014 (weather at means) C 43231.93 9063.392 4.769951 0.0001 REAL_ELECTRICITY_PRICE(-2) -2880.37 494.0632 -5.829963 0.0000 -0.2313 REAL_GSP 0.182654 0.028914 6.317075 0.4319 CDD 4.680232 2.400735 1.9495 0.0662 0.1765 HDD 6.425083 1.936429 3.318006 0.0036 0.1269 R-squared 0.942101 Mean dependent var 77111.55 Adjusted R-squared 0.929912 S.D. dependent var 7050.546 S.E. of regression 1866.571 Durbin-Watson stat 1.716599 F-statistic 77.28978 Prob(F-statistic) 0.000000

Louisiana Retail Sales (GWh) 2015 Forecast 2016-2025 projected CAGR – 1.87% 2016 Forecast 2017-2026 projected CAGR – 0.62% 1990-2014 actual CAGR – 1.47%

Elasticity at 2014 (weather at means) Louisiana Model Option 2 Electricity sales + CHP as dependent variable GSP excluding Mining GSP (oil and gas drilling)as macro-economic variable Dependent Variable: ELECTRICITY_SALES+CHP Method: Least Squares Sample: 1990 2014 Included observations: 25   Variable Coefficient Std. Error t-Statistic Prob. Elasticity at 2014 (weather at means) C 58418.34 16094.09 3.629801 0.0017 REAL_ELECTRICITY_PRICE(-2) -4559.912 857.4264 -5.318138 0.0000 -0.2716 REAL_GSP_EXCLUDE_MINING 0.281547 0.038688 7.277368 0.4617 CDD 6.894593 4.106805 1.678822 0.1087 0.2010 HDD 7.988799 3.567171 2.239534 0.0366 0.1212 R-squared 0.927427 Mean dependent var 99988.41 Adjusted R-squared 0.912912 S.D. dependent var 11740.93 S.E. of regression 3464.826 Durbin-Watson stat 1.435208 F-statistic 63.8959 Prob(F-statistic) 0.000000

Louisiana Retail Sales (GWh) 2015 Retail Sales Forecast 2016-2025 projected CAGR – 1.87% 2016 Retail Sales Forecast 2017-2026 projected CAGR – 0.95% 1990-2014 actual CAGR – 1.47%

Pros & Cons Option 1 Option 2 Consistent with approach in other states and preference for public data sources Growth may be low considering expected new loads; some indication that all may not show up or be delayed Option 2 Relies on arbitrary assumption that CHP will remain constant across forecast horizon Relies on non-standard economic driver Results in slightly higher forecast

Other Option We could incorporate a load adjustment for new large loads that have a high degree of certainty We would need information on the size and timing of those loads

Allocation Factors

Reminder Allocation factors are used to assign state forecasts to particular LRZs Small loads in Oklahoma & Tennessee that are in LRZ 8 are included with Arkansas Kentucky is combined with Indiana and Montana is combined with North Dakota per MISO request Illinois and Missouri were also analyzed at the Chicago and St. Louis Metropolitan Statistical Area

Historical Load Shares (MISO vs. non-MISO) State MISO Sales (Megawatthours) Non-MISO Sales (Megawatthours) 2010 2011 2012 2013 2014 Average AR 34,008,348 13,071,953 70.57% 70.39% 70.52% 70.45% 72.23% 70.83% IA 43,920,136 3,281,717 92.92% 93.04% 93.22% 93.05% 93.03% IL 49,309,489 92,230,798 34.55% 34.80% 33.91% 34.59% 34.84% 34.54% IN 85,104,043 21,838,461 78.92% 79.00% 79.22% 79.58% 79.19% KY 11,400,836 67,438,088 11.87% 12.43% 12.82% 13.51% 14.46% 13.02% IN+KY 96,504,879 89,276,549 47.49% 48.49% 48.78% 49.94% 51.95% 49.33% LA 83,987,702 6,640,614 91.77% 91.74% 92.06% 92.20% 92.67% 92.09% MI 99,263,860 4,050,238 96.01% 96.16% 96.21% 96.10% 96.08% 96.11% MN 67,870,999 848,368 98.73% 98.84% 98.75% 98.77% 98.76% MO 41,332,442 42,545,955 49.67% 49.47% 50.33% 49.52% 49.28% 49.65% MS 22,014,990 27,393,641 45.89% 45.24% 44.78% 44.73% 44.56% 45.04% MT 803,729 13,298,663 5.36% 5.40% 5.55% 5.57% 5.70% 5.51% ND 10,935,451 7,304,281 71.49% 66.16% 65.40% 59.95% 66.71% ND+MT 11,739,180 20,602,944 37.35% 37.90% 36.76% 37.46% 36.30% 37.15% SD 3,120,871 9,233,855 26.87% 26.07% 26.02% 25.32% 25.26% 25.91% TX 21,815,593 367,854,227 5.66% 5.46% 5.99% 5.74% 5.60% 5.69% WI 69,494,755 100.0%

Historical Load Shares (LRZ) MISO LRZ State State Level MISO Load Fraction Average 2010 2011 2012 2013 2014 1 IA 1.77% 1.76% 1.73% 1.78% 1.83% IL 0.0002% MI 0.14% 0.13% MN 96.81% 96.73% 96.76% 96.93% 96.89% ND+MT 37.15% 37.35% 37.90% 36.76% 37.46% 36.30% SD 24.10% 24.97% 24.28% 24.24% 23.51% WI 16.77% 16.59% 16.94% 16.23% 17.02% 17.05% 2 5.09% 5.22% 5.28% 4.89% 4.94% 5.14% 83.23% 83.41% 83.06% 83.77% 82.98% 82.95% 3 91.25% 91.14% 91.28% 91.48% 91.15% 91.22% 1.42% 1.45% 1.40% 1.95% 2.00% 1.97% 1.91% 1.86% 2.01% 1.81% 1.90% 1.79% 1.80% 1.75% 4 33.11% 33.12% 33.35% 32.49% 33.17% 33.44% 5 MO 49.41% 49.22% 50.09% 49.28% 49.06% 6 IN+KY 49.29% 47.49% 48.49% 48.60% 49.94% 51.95% 7 90.88% 90.65% 90.75% 91.19% 91.02% 90.80% 8 AR 70.83% 70.57% 70.39% 70.52% 70.45% 72.23% 0.24% 0.26% 0.25% 0.23% 0.22% TX 0.01% 9 LA 92.09% 91.77% 91.74% 92.06% 92.20% 92.67% 5.68% 5.65% 5.46% 5.98% 5.73% 5.59% 10 MS 45.04% 45.89% 45.24% 44.78% 44.73% 44.56%

2016 Allocation Factors With the exception of IN+KY and MT+ND, all allocation factors were determined from the average of the 2010 – 2014 load shares Chicago MSA has a similar growth as Illinois for GSP, so no adjustment St. Louis MSA has lower population but higher non-manufacturing employment, which tend to cancel each other out, so no adjustment

IN+KY The closure of the Paducah Gaseous Diffusion Plant in non-MISO Kentucky (mid-year 2013) caused a shift in the LRZ 6 load share for IN+KY We use the 2014 load share, since it is the first year where the plant is shut down for the entire year

MT+ND Growth and contraction in the Bakken region, which results in a spike in statewide consumption in 2014, occurs primarily outside the MISO region This volatility should not be reflected in the LRZ 1 forecast We use the average of 2010 – 2013 load factors to determine the allocation factor

MISO Allocation Factors - AR

MISO Allocation Factors - IA

MISO Allocation Factors - IL

MISO Allocation Factors – IN+KY

MISO Allocation Factors – LA

MISO Allocation Factors – MI

MISO Allocation Factors – MN

MISO Allocation Factors – MO

MISO Allocation Factors – MS

MISO Allocation Factors – ND+MT

MISO Allocation Factors – SD

MISO Allocation Factors – TX

MISO Allocation Factors – WI

Next Steps

September Workshop We will present updated energy to peak conversion We will cover the draft forecast results at LRZ and MISO level Please send your comments and suggestions for improvements